The weekend effect in geriatric traumatic brain injury in tertiary hospital: an observational study
Bibliographic record
Abstract
Abstract Introduction There were no previous studies discussing the comparison of the complications among traumatic brain injury (TBI) cases during weekdays and weekends. The current study aims to retrospectively compare the TBI outcome of geriatric patients on weekdays versus weekends in the neurosurgery department in a tertiary hospital in Oman. Methods This is a retrospective study, from December 2015 to December 2019. Medical records of 670 patients above 65 years and admitted to the neurosurgery ward were reviewed. From that, only 45 patients over 65 years, diagnosed with TBI and managed surgically were included. Results The study included 28 patients admitted during weekdays and 17 patients admitted during weekends. Nevertheless, the highest number of admissions was during Friday. The male-to-female ratio was 3.6:1 during weekdays and 3.2:1 during weekends. The average length of stay (LOS) was 12.4 days among patients operated on weekdays compared to 36.5 days on weekends. For average ICU stay, it was 3.9 days during weekdays compared to 32.2 during weekends (p = 0.011). Complications were found to be more common among patients admitted on weekends (p = 0.015). Conclusion Significant differences between weekdays and weekends were found. So, more trauma imaging facilities and neurosurgeons need to be available during the weekends.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".